cyc-gbm

cyc-gbm

A package for the Cyclic Gradient Boosting Machine algorithm. For the (pre-print) paper describing the algorithm, see here.

Installation

You can install the package using pip:

pip install cyc-gbm

Alternatively, you can install the package from source. This will also include a pipeline for reproducing the results in the paper. Follow these steps:

• Clone this repository to your local machine:

  git clone https://github.com/henningzakrisson/cyc-gbm.git
  

• Create a virtual environment in the root directory of the repository:

  python3 -m venv venv
  

• Activate the virtual environment:

  source venv/bin/activate
  

• Install the required dependencies:

  pip install -r requirements.txt
  

Usage example

Fitting the mean and (log) sigma parameters of a normal distribution to a simulated dataset:

import numpy as np
from cyc_gbm import CyclicalGradientBooster
from sklearn.model_selection import train_test_split

# Simulate data
X = np.random.normal(size=(1000, 2))
mu = X[:, 0] + 10 * (X[:, 1] > 0)
sigma = np.exp(3 - 2 * (X[:, 0] > 0))
y = np.random.normal(mu, sigma)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Fit model
model = CyclicalGradientBooster(
   distribution='normal',
   learning_rate=0.1,
   n_estimators=[26, 34],
   min_samples_split = 2,
   min_samples_leaf=20,
   max_depth=2,

)
model.fit(X_train, y_train)

# Evaluate
loss = model.dist.loss(y=y_test, z=model.predict(X_test)).sum()
print(f'negative log likelihood: {loss}')

Contact

If you have any questions, feel free to contact me here.